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Ranking Multivariate GARCH Models by Problem Dimension

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  • Massimiliano Caporin

    ()
    (Università di Padova)

  • Michael McAleer

    ()
    (Erasmus University Rotterdam)

Abstract

In the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Some recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC, Exponentially Weighted Moving Average, and covariance shrinking, using the historical data of 89 US equities. Our methods follow some of the approach described in Patton and Sheppard (2009), and contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.

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Paper provided by Dipartimento di Scienze Economiche "Marco Fanno" in its series "Marco Fanno" Working Papers with number 0124.

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Length: 43 pages
Date of creation: Dec 2010
Date of revision:
Handle: RePEc:pad:wpaper:0124

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Keywords: Covariance forecasting; model confidence set; model ranking; MGARCH; model comparison.;

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Citations

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Cited by:
  1. Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2013. "On the Benefits of Equicorrelation for Portfolio Allocation," NCER Working Paper Series, National Centre for Econometric Research 99, National Centre for Econometric Research.
  2. Manner, Hans & Reznikova, Olga, 2010. "Forecasting international stock market correlations: does anything beat a CCC?," Discussion Papers in Statistics and Econometrics 7/10, University of Cologne, Department for Economic and Social Statistics.
  3. Jeroen V.K. Rombouts & Lars Stentoft & Francesco Violante, 2012. "The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average options," CREATES Research Papers 2012-04, School of Economics and Management, University of Aarhus.
  4. Adam E Clements & Mark Doolan & Stan Hurn & Ralf Becker, 2012. "Selecting forecasting models for portfolio allocation," NCER Working Paper Series, National Centre for Econometric Research 85, National Centre for Econometric Research.
  5. Massimiliano Caporin & Michael McAleer, 2010. "Model Selection and Testing of Conditional and Stochastic Volatility Models," KIER Working Papers, Kyoto University, Institute of Economic Research 724, Kyoto University, Institute of Economic Research.
  6. Diego Fresoli & Esther Ruiz, 2014. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de Estadística y Econometría ws140202, Universidad Carlos III, Departamento de Estadística y Econometría.
  7. Adam E Clements & Ayesha Scott & Annastiina Silvennoinen, 2012. "Forecasting multivariate volatility in larger dimensions: some practical issues," NCER Working Paper Series, National Centre for Econometric Research 80, National Centre for Econometric Research.

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